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Multiple Kernel Based Regularized System Identification with SURE Hyper-parameter Estimator
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- Hong, Shiying (author)
- Chinese Univ Hong Kong, Peoples R China; Chinese Univ Hong Kong, Peoples R China
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- Mu, Biqiang (author)
- Linköpings universitet,Reglerteknik,Tekniska fakulteten
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- Yin, Feng (author)
- Chinese Univ Hong Kong, Peoples R China
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- (creator_code:org_t)
- ELSEVIER SCIENCE BV, 2018
- 2018
- English.
- In: 18th IFAC Symposium on System Identification (SYSID), Proceedings. - : ELSEVIER SCIENCE BV. ; , s. 13-18
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- In this work, we study the multiple kernel based regularized system identification with the hyper-parameter estimated by using the Steins unbiased risk estimators (SURE). To approach the problem, a QR factorization is first employed to compute SUREs objective function and its gradient in an efficient and accurate way. Then we propose an algorithm to solve the SURE problem, which contains two parts: the outer optimization part and the inner optimization part. For the outer optimization part, the coordinate descent algorithm is used and for the inner optimization part, the projection gradient algorithm is used. Finally, the efficacy of the proposed algorithm is demonstrated by numerical simulations. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Subject headings
- NATURVETENSKAP -- Matematik -- Beräkningsmatematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Computational Mathematics (hsv//eng)
Keyword
- Linear system identification; regularization methods; hyper-parameter estimation; SURE; multiple kernel
Publication and Content Type
- ref (subject category)
- kon (subject category)
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